import gradio as gr
import hopsworks
import joblib
import pandas as pd
import numpy as np
import folium
import sklearn.preprocessing as proc
import json
import time
from datetime import timedelta, datetime
from branca.element import Figure

#from functions import decode_features, get_weather_data, get_weather_df, get_weather_json_quick
#import functions

def greet(total_pred_days):

    
#   print("hi")
    project = hopsworks.login()
#   print("connected")
#   #api = project.get_dataset_api()
#   

#   # The latest available data timestamp
#   start_time = 1649196000000

#  # end_time = 1670972400000

    #start_date = datetime.now() - timedelta(days=1)
    #start_time = int(start_date.timestamp()) * 1000

    #print("Time Stamp Set. ")

    #print("latest_date")
    
    mr=project.get_model_registry()
    model = mr.get_model("temp_model_new", version=1)
    model_dir=model.download()
    
    model1 = mr.get_model("tempmax_model_new", version=1) 
    model_dir1=model1.download()

    model2 = mr.get_model("tempmin_model_new", version=1)
    model_dir2=model2.download()
    
    model = joblib.load(model_dir + "/model_temp_new.pkl")
    model1 = joblib.load(model_dir1 + "/model_tempmax_new.pkl")
    model2 = joblib.load(model_dir2+ "/model_tempmin_new.pkl")

    print("temp_model is now right")
         
    
    #X = feature_view.get_batch_data(start_time=start_time)
    
    #latest_date_unix = str(X.datetime.values[0])[:10]
    #latest_date = time.ctime(int(latest_date_unix))
    # cities = [city_tuple[0] for city_tuple in cities_coords.keys()]

    
    str1 = ""
    
    if(total_pred_days == ""):
        return "Empty input"
    
    count = int(total_pred_days)
    if count > 14:
        str1 += "Warning: 14 days at most. " + '\n'
        count = 14
    if count <0:
        str1 = "Invalid input."
        return str1

    # Get weather data
    fs = project.get_feature_store()
    print("get the store")
    feature_view = fs.get_feature_view(
        name = 'weathernew_fv',
        version = 1
    )
    print("get the fv")
    
    
    global X
    X = pd.DataFrame()
    
    for i in range(count+1):
        # Get, rename column and rescale
        next_day_date = datetime.today() + timedelta(days=i)
        next_day = next_day_date.strftime ('%Y-%m-%d')
        print(next_day)
        json = get_weather_json_quick(next_day)
        temp = get_weather_data(json)
        print("Raw data")
        print(temp)
        X = X.append(temp, ignore_index=True)
    
    
    # X reshape
    
    X.drop('preciptype', inplace = True, axis = 1)
    X.drop('severerisk', inplace = True, axis = 1)
    X.drop('stations', inplace = True, axis = 1)
    X.drop('sunrise', inplace = True, axis = 1)
    X.drop('sunset', inplace = True, axis = 1)
    X.drop('moonphase', inplace = True, axis = 1)
    X.drop('description', inplace = True, axis = 1)
    X.drop('icon', inplace = True, axis = 1)
    
    X = X.drop(columns=["datetime", "temp", "tempmax", "tempmin", "sunriseEpoch", "sunsetEpoch", "source", "datetimeEpoch", ]).fillna(0) 
    X = X.rename(columns={'pressure':'sealevelpressure'})
    X = X.drop(columns = ['conditions'])
    print("Check dataframe")
    print(X)
    print("Data batched.")
    # Rescale
    #X = decode_features(X, feature_view=feature_view)
    # Data scaling
    #category_cols = ['name','datetime','conditions', 'tempmin', 'tempmax', 'temp']

    #mapping_transformers = {col_name:fs.get_transformation_function(name='standard_scaler') for col_name in col_names if col_name not in category_cols}
    #category_cols = {col_name:fs.get_transformation_function(name='label_encoder') for col_name in category_cols if col_name not in ['datetime', 'tempmin', 'tempmax', 'temp']}

    #mapping_transformers.update(category_cols)
    
    
    # Data scaling
    #category_cols = ['conditions']
    cat_std_cols = ['feelslikemax','feelslikemin','feelslike','dew','humidity','precip','precipprob','precipcover','snow','snowdepth','windgust','windspeed','winddir','sealevelpressure','cloudcover','visibility','solarradiation','solarenergy','uvindex']
    scaler_std = proc.StandardScaler()
    #scaler_lb = proc.LabelEncoder()
    X.insert(19,"conditions",0)
    X[cat_std_cols] = scaler_std.fit_transform(X[cat_std_cols])
    #X[category_cols] = scaler_std.transform(X[category_cols])
    X.insert(0,"name",0)
    # Predict
    preds = model.predict(X)
    preds1= model1.predict(X)
    preds2= model2.predict(X)
    
    for x in range(count):
        if (x != 0):
            str1 += (datetime.now() + timedelta(days=x)).strftime('%Y-%m-%d') + " predicted temperature:      " +str(int(preds[len(preds) - count + x]))+ " predicted max temperature:      " +str(int(preds1[len(preds1) - count + x]))+ " predicted min temperature:      " +str(int(preds2[len(preds2) - count + x]))+"\n"
    
    #print(str1)
    return str1

demo = gr.Interface(fn=greet, inputs = "text", outputs="text")


    
if __name__ == "__main__":
    demo.launch()